Person–Job Fit (PJF) serves as a core task of intelligent recruitment recommendation. However, existing graph-based PJF models rely on a fixed, single-path aggregation scheme, thereby failing to simultaneously capture global interaction statistics and local competency-matching signals from candidate–job bipartite graphs. To address this limitation, this work proposes Bi-path Graph Convolutional Neural Network with Gate Fusion (BiGCNG), a dual-path graph convolutional network with global learnable gate fusion, composed of three coordinated modules. First, the Shared Text Embedding Pre-processing Module (STEPM) generates unified node embeddings by fusing structured attributes and BERT contextual text features. Second, the Bi-path Graph Convolution Module (BiGCM) extracts multi-granularity graph representations via separate sum and max aggregation paths. Third, the lightweight Gate Fusion Module (GFM) balances two feature streams via a learnable global scalar gate. The model is optimized with regularized Bayesian Personalized Ranking (BPR) loss on highly sparse recruitment data (99.97% sparsity). BiGCNG is evaluated on the Zhilian dataset, a real-world Chinese recruitment dataset, and outperforms five mainstream baselines notably, increasing MRR@5 by 7.67% and NDCG@5 by 5.48% on the Candidate subset, 2.30% and 0.61% on the Job subset against the best baseline, respectively. Several visualizations and hyperparameter analysis jointly validate the effectiveness and robustness of dual-path propagation and gate fusion. This work provides an effective multi-granularity graph learning paradigm for intelligent talent recruitment matching.

The travel problem of visually impaired people is a worldwide issue that needs urgent attention. Although intelligent guide sticks provide obstacle detection and early warning through multi-sensor fusion and embedded algorithms, existing systems generally cannot model the temporal movement patterns of dynamic obstacles, such as pedestrians and vehicles, thereby hindering intention prediction and active obstacle avoidance. Long short-term memory (LSTM), with its gating mechanism, effectively captures long-term dependencies in trajectories and offers a promising solution. This review compares and analyzes LSTM against other mainstream temporal models under the resource constraints of intelligent guide sticks and finds that LSTM demonstrates a favorable combination in temporal modeling capability, lightweight maturity, and edge deployment feasibility. We categorize five lightweight techniques—architecture simplification, low-rank decomposition, structured pruning, quantization, and knowledge distillation—and examine their compression effectiveness, accuracy preservation, and hardware applicability across typical platforms. Furthermore, this review surveys application cases in speech guidance, trajectory prediction-based obstacle avoidance, positioning and navigation, edge computing, and Internet collaboration, exploring the diverse potential of LSTM in intelligent guide stick scenarios. The findings indicate that, after lightweight processing, LSTM models can meet the deployment requirements of resource-constrained edge devices, suggesting their potential feasibility on resource-constrained hardware platforms. However, existing applications still face challenges in balancing real-time performance and accuracy, meeting stringent resource constraints, and the absence of end-to-end validation on real intelligent guide stick prototypes. The reviewed evidence suggests that LSTM-based prediction represents a promising and practically valuable pathway for transitioning intelligent guide sticks from passive response to active prediction. Future research should prioritize real-world deployment validation, domain-specific data collection, and hardware-software co-design to realize its potential fully.